{"id":"W3129690166","doi":"10.1155/2021/5597130","title":"Calibrating Path Choices and Train Capacities for Urban Rail Transit Simulation Models Using Smart Card and Train Movement Data","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Massachusetts Institute of Technology","keywords":"Computer science; Transit (satellite); Sensitivity (control systems); Randomness; Path (computing); Data collection; Urban rail transit; Simulation; Calibration; Transport engineering; Public transport; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006541531,0.0001162079,0.0002289277,0.00008418071,0.0003241924,0.00006901653,0.00007048106,0.00007845061,0.000004464952],"category_scores_gemma":[0.00007218347,0.0001226279,0.00005125278,0.0001531312,0.00008726987,0.002198291,9.688204e-7,0.0001005804,1.205971e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000354858,"about_ca_system_score_gemma":0.000182229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009816072,"about_ca_topic_score_gemma":0.0008591296,"domain_scores_codex":[0.9986957,0.00008523234,0.0005239811,0.0001900236,0.00032468,0.0001803917],"domain_scores_gemma":[0.9989583,0.0002413651,0.0003440768,0.00007833505,0.0002657976,0.0001121319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009229326,0.00003033944,0.003044444,0.00009313205,0.0000409584,0.000005319623,0.07888392,0.9113473,0.001736177,0.002178238,0.000007406164,0.002540426],"study_design_scores_gemma":[0.007605644,0.000350577,0.07650895,0.001033444,0.0009254569,0.000007433111,0.1895166,0.7063026,0.001512548,0.01082203,0.004457867,0.0009568323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5381836,0.0007499366,0.4602034,0.0002553432,0.0001634361,0.0001819417,0.0002241892,0.00001561577,0.00002247051],"genre_scores_gemma":[0.9407096,0.0003468338,0.05828841,0.0001145079,0.0001508347,0.00000310535,0.0003382592,0.00001506096,0.00003338727],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.402526,"threshold_uncertainty_score":0.5000622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06614983674856525,"score_gpt":0.3238522492670698,"score_spread":0.2577024125185046,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}